| dc.description.abstract | The manufacturing industry is facing the problems of aging population, insufficient manpower, rising wage costs, diversity of production products, shortening of product cycles, drastic changes in global market demand, etc., so who can sensitively respond to market changes and quickly produce diverse commodities, who is The winner of the market. In order to overcome this problem, the manufacturing industry actively introduces Industry 4.0 and gradually develops into smart factories. The factory′s physical networking, virtual and real integration, and the application of smart devices and robots will enable multiple types of devices to be connected to each other and communicate with each other. To make the production process more flexible, to assist the manufacturing industry to solve business difficulties and enhance competitiveness. Smart factories have become a modern manufacturing industry trend, but the collection of large amounts of data in the manufacturing process will cause the manufacturing management method to be different from the current situation. It is no longer possible to manually process, analyze and organize it into effective information in a short time. The main reason for the difficulty in managing is the amount of data, diversity, speed and authenticity of data input and output, and it is difficult for managers to issue effective countermeasures in real time. Therefore, there are huge amounts of data in smart factories to construct multiple types of databases. How can managers use Data Mining to quickly obtain valuable information to judge and issue countermeasures, or the equipment can quickly find the best method and quickly adjust through Machine Learning. Modern management must learn courses.
This study is directed to Printed Circuit Board and PCB industry. Take U company as an example to discuss how to build a smart factory through machine networking, use the large amount of data collected in the database, and apply data exploration technology through huge amounts of data to provide on-site operators with rapid Obtain effective information to adjust the best production parameters and improve productivity and efficiency. | en_US |